2) We attempt to show that the concept of a context discriminator can be extended to existing GAN based works for MRI reconstruction. The different GAN based architectures and their corresponding loss functions can be found in Table REF . In this experiment to ensure a fair comparison, the generator is set to U-Net, discriminator is set to global feature extractor(\(\Psi _{G}\) ) followed by a classifier(\(\Psi _{C}\) )(basic discriminator) and the loss functions are taken from their respective works [1]}, [2]}, [3]}, [4]}. This arrangement means that the difference between the various GAN based architectures comes only from the generator loss. In this experiment, we replace the basic discriminator of the GAN architectures with our proposed context discriminator. The results comparing the GAN architectures with basic discriminator and context discriminator are reported in Table REF . From the Table, it is clear that the GAN with context discriminator have shown improved results compared to GAN with basic discriminator for different generator loss. A few sample results comparing the GAN based reconstruction methods with basic and context discriminator are shown in Figure REF . From the figure we observe that the ROI's reconstruction error for GAN with context discriminator is lesser compared to GAN with the basic discriminator. This shows that the context discriminator can be extended to other GAN based reconstruction methods.
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